Children who experienced a repeated event only appear less accurate in a second interview than those who experienced a unique event.
Bibliographic record
Abstract
When children have experienced a repeated event, reports of experienced details may be inconsistently reported across multiple interviews. In 3 experiments, we explored consistency of children's reports of an instance of a repeated event after a long delay (Exp. 1, N = 53, Mage = 7.95 years; Exp. 2, N = 70, Mage = 5.77 years, Exp. 3, N = 59, Mage = 4.88 years). In all experiments, children either experienced 1 or 4 activity sessions, followed at a relatively short delay (days or weeks) by an initial memory test. Then, following a longer delay (4 months or 1 year), children were reinterviewed with the same memory questions. We analyzed the consistency of children's memory reports across the 2 interviews, as well as forgetting, reminiscence, and accuracy, defined with both narrow and broad criteria. A highly consistent pattern was observed across the 3 experiments with children who experienced a single event appearing more consistent than children who experienced a repeated event. We conclude that inconsistencies across multiple interviews can be expected from children who have experienced repeated events and these inconsistencies are often reflective of accurate, but different, recall. (PsycINFO Database Record
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".